Economic and financial optimization and planning is commonly used to estimate or predict and forecast the performance and outcome of real systems, given specific sets of input data of interest. An economic-based system will have many variables, agents, and influences that determine its behavior and performance. In one sense, it is relatively straightforward, in the past tense, to review historical data, understand its past performance, and state with relative certainty that the system's past behavior was indeed driven by the historical data. A much more difficult task, but one that is extremely valuable, is to generate a mathematical model of the system which predicts with high degree of certainty how the system will behave or would have behaved, with different sets of data and assumptions. While forecasting and fitting using different sets of input data is inherently imprecise, i.e., no model can achieve 100% certainty, the field of probability and statistics has provided many tools which allow such predictions to be made with reasonable certainty and acceptable levels of confidence.
In its basic form, the economic model can be viewed as a predicted or anticipated outcome of a mathematical expression, as driven by a given set of input data and assumptions. The input data is processed through the mathematical expression representing either the expected or current behavior of the real system. The mathematical expression is formulated or derived from principles of probability and statistics, often by analyzing historical data and corresponding known outcomes, to achieve a best fit of the expected behavior of the system to other sets of data, both in terms of forecasting and fitting. In other words, the model should be able to predict the outcome or response of the system to a specific set of data being considered or proposed, within a level of confidence, or an acceptable level of uncertainty.
Economic modeling has many uses and applications. One emerging area in which modeling has exceptional promise is the financial services industry. Banks, credit unions, savings and loan, commercial lenders, investment houses, and brokerage firms face stiff competition for limited customers and business. Most if not all financial services institutions make every effort to maximize sales, volume, revenue, and profit. Economic modeling can be an effective tool in helping management to achieve these important goals.
One modeling tool of use to financial services institutions involves estimating pricing sensitivities or elasticities of consumers' demand for financial products, such as depository products, loans, mortgages, credit cards, investments, and insurance contracts. The process of setting pricing components of the financial contracts, such as interest rates, applicable fees, durations, penalties, and balances, is an essential task for financial services institutions that can determine most granular characteristics of underlying portfolios performances. Some large institutions have used sophisticated analytics and modeling to understand demand trends and uncover areas of profit opportunity. Automated pricing software represents a movement toward greater precision in the pricing process. The software relies on complex demand models to estimate customers' attitudes toward pricing and the sensitivities of demand from historical sales data. The demand models create parameters which can be used to optimize pricing practices for each portfolio segment level and to generate portfolio performance analysis and forecasts.
The financial services institution typically offers a large portfolio of financial products. Each financial product and service has its own unique set of attributes and variables that control pricing and demand. Attributes are the criteria that define a financial product or pricing segment, such as the credit score of the customer or term of the instrument. Variables are the price defining values of the financial product, such as interest rates, reward points, and fees that can be changed to impact KPIs. Each category of financial products and services in the large portfolio is managed by a different group or person within the institution. In most cases, there is little cross-over between the different categories of financial products and services in terms of modeling resources and management strategies. The upper management of the financial services institution sets the targets and goals for each financial product manager toward the institution's overall business plan. Yet, the implementation to achieve the directives remains largely up to the product manager's discretion. In other words, each product manager develops his or her own strategy and utilizes available resources according to the unique set of attributes associated with the financial products and services in his or her care. The financial services institution must maintain a variety of dedicated or specialized modeling resources, each as requested by the different product managers, to meet the goals. In some cases, the product manager must use a modeling resource which is not necessarily optimized for their responsible products and services. The use of dedicated or specialized modeling resources adds costs to the management process and reduces potential profitability due to the lack of correlation between the modeling resources and coordination between the product managers.